Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1017
Missing cells359
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.5 KiB
Average record size in memory88.1 B

Variable types

Categorical2
Numeric9

Alerts

country is highly overall correlated with country_codeHigh correlation
country_code is highly overall correlated with countryHigh correlation
raw_wage_gap_ratio_decile_1 is highly overall correlated with raw_wage_gap_ratio_mean and 4 other fieldsHigh correlation
raw_wage_gap_ratio_decile_9 is highly overall correlated with raw_wage_gap_ratio_mean and 5 other fieldsHigh correlation
raw_wage_gap_ratio_mean is highly overall correlated with raw_wage_gap_ratio_decile_1 and 6 other fieldsHigh correlation
raw_wage_gap_ratio_median is highly overall correlated with raw_wage_gap_ratio_decile_1 and 7 other fieldsHigh correlation
wage_gap_ratio_decile_1 is highly overall correlated with raw_wage_gap_ratio_decile_1 and 4 other fieldsHigh correlation
wage_gap_ratio_decile_9 is highly overall correlated with raw_wage_gap_ratio_decile_9 and 5 other fieldsHigh correlation
wage_gap_ratio_mean is highly overall correlated with raw_wage_gap_ratio_decile_1 and 7 other fieldsHigh correlation
wage_gap_ratio_median is highly overall correlated with raw_wage_gap_ratio_decile_1 and 7 other fieldsHigh correlation
year is highly overall correlated with raw_wage_gap_ratio_decile_9 and 4 other fieldsHigh correlation
wage_gap_ratio_median has 46 (4.5%) missing valuesMissing
raw_wage_gap_ratio_median has 49 (4.8%) missing valuesMissing
wage_gap_ratio_decile_1 has 64 (6.3%) missing valuesMissing
raw_wage_gap_ratio_decile_1 has 64 (6.3%) missing valuesMissing
wage_gap_ratio_decile_9 has 58 (5.7%) missing valuesMissing
raw_wage_gap_ratio_decile_9 has 58 (5.7%) missing valuesMissing
raw_wage_gap_ratio_mean has 53 (5.2%) zerosZeros
raw_wage_gap_ratio_decile_1 has 24 (2.4%) zerosZeros

Reproduction

Analysis started2025-11-10 18:10:12.923725
Analysis finished2025-11-10 18:10:22.242363
Duration9.32 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

country_code
Categorical

High correlation 

Distinct50
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
GBR
 
55
USA
 
52
AUS
 
50
JPN
 
50
FIN
 
45
Other values (45)
765 

Length

Max length8
Median length3
Mean length3.1996067
Min length3

Characters and Unicode

Total characters3254
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARG
2nd rowARG
3rd rowARG
4th rowARG
5th rowARG

Common Values

ValueCountFrequency (%)
GBR55
 
5.4%
USA52
 
5.1%
AUS50
 
4.9%
JPN50
 
4.9%
FIN45
 
4.4%
NZL41
 
4.0%
KOR41
 
4.0%
POL40
 
3.9%
HUN34
 
3.3%
SWE33
 
3.2%
Other values (40)576
56.6%

Length

2025-11-10T18:10:22.346173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gbr55
 
5.4%
usa52
 
5.1%
aus50
 
4.9%
jpn50
 
4.9%
fin45
 
4.4%
nzl41
 
4.0%
kor41
 
4.0%
pol40
 
3.9%
hun34
 
3.3%
swe33
 
3.2%
Other values (40)576
56.6%

Most occurring characters

ValueCountFrequency (%)
E297
 
9.1%
U280
 
8.6%
N269
 
8.3%
R248
 
7.6%
S206
 
6.3%
A196
 
6.0%
O195
 
6.0%
C191
 
5.9%
L190
 
5.8%
D126
 
3.9%
Other values (19)1056
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E297
 
9.1%
U280
 
8.6%
N269
 
8.3%
R248
 
7.6%
S206
 
6.3%
A196
 
6.0%
O195
 
6.0%
C191
 
5.9%
L190
 
5.8%
D126
 
3.9%
Other values (19)1056
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E297
 
9.1%
U280
 
8.6%
N269
 
8.3%
R248
 
7.6%
S206
 
6.3%
A196
 
6.0%
O195
 
6.0%
C191
 
5.9%
L190
 
5.8%
D126
 
3.9%
Other values (19)1056
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E297
 
9.1%
U280
 
8.6%
N269
 
8.3%
R248
 
7.6%
S206
 
6.3%
A196
 
6.0%
O195
 
6.0%
C191
 
5.9%
L190
 
5.8%
D126
 
3.9%
Other values (19)1056
32.5%

country
Categorical

High correlation 

Distinct50
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
United Kingdom
 
55
United States
 
52
Australia
 
50
Japan
 
50
Finland
 
45
Other values (45)
765 

Length

Max length37
Median length15
Mean length9.2350049
Min length4

Characters and Unicode

Total characters9392
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArgentina
2nd rowArgentina
3rd rowArgentina
4th rowArgentina
5th rowArgentina

Common Values

ValueCountFrequency (%)
United Kingdom55
 
5.4%
United States52
 
5.1%
Australia50
 
4.9%
Japan50
 
4.9%
Finland45
 
4.4%
New Zealand41
 
4.0%
Korea41
 
4.0%
Poland40
 
3.9%
Hungary34
 
3.3%
Sweden33
 
3.2%
Other values (40)576
56.6%

Length

2025-11-10T18:10:22.430234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united107
 
7.5%
european58
 
4.0%
oecd58
 
4.0%
countries58
 
4.0%
union58
 
4.0%
kingdom55
 
3.8%
states52
 
3.6%
australia50
 
3.5%
japan50
 
3.5%
finland45
 
3.1%
Other values (48)843
58.8%

Most occurring characters

ValueCountFrequency (%)
a1061
 
11.3%
n897
 
9.6%
e760
 
8.1%
i680
 
7.2%
r485
 
5.2%
o476
 
5.1%
t439
 
4.7%
417
 
4.4%
d392
 
4.2%
l385
 
4.1%
Other values (42)3400
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)9392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1061
 
11.3%
n897
 
9.6%
e760
 
8.1%
i680
 
7.2%
r485
 
5.2%
o476
 
5.1%
t439
 
4.7%
417
 
4.4%
d392
 
4.2%
l385
 
4.1%
Other values (42)3400
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1061
 
11.3%
n897
 
9.6%
e760
 
8.1%
i680
 
7.2%
r485
 
5.2%
o476
 
5.1%
t439
 
4.7%
417
 
4.4%
d392
 
4.2%
l385
 
4.1%
Other values (42)3400
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1061
 
11.3%
n897
 
9.6%
e760
 
8.1%
i680
 
7.2%
r485
 
5.2%
o476
 
5.1%
t439
 
4.7%
417
 
4.4%
d392
 
4.2%
l385
 
4.1%
Other values (42)3400
36.2%

year
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.4287
Minimum1970
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2025-11-10T18:10:22.525970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1983
Q12000
median2010
Q32017
95-th percentile2023
Maximum2024
Range54
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.0738
Coefficient of variation (CV)0.00601456
Kurtosis-0.023896363
Mean2007.4287
Median Absolute Deviation (MAD)8
Skewness-0.77215942
Sum2041555
Variance145.77666
MonotonicityNot monotonic
2025-11-10T18:10:22.671918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201849
 
4.8%
202249
 
4.8%
201447
 
4.6%
201046
 
4.5%
202345
 
4.4%
200643
 
4.2%
200240
 
3.9%
202033
 
3.2%
202132
 
3.1%
201929
 
2.9%
Other values (45)604
59.4%
ValueCountFrequency (%)
19701
 
0.1%
19711
 
0.1%
19721
 
0.1%
19732
 
0.2%
19742
 
0.2%
19754
0.4%
19764
0.4%
19775
0.5%
19785
0.5%
19795
0.5%
ValueCountFrequency (%)
202416
 
1.6%
202345
4.4%
202249
4.8%
202132
3.1%
202033
3.2%
201929
2.9%
201849
4.8%
201729
2.9%
201629
2.9%
201528
2.8%

wage_gap_ratio_mean
Real number (ℝ)

High correlation 

Distinct1005
Distinct (%)99.8%
Missing10
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean19.90531
Minimum-0.2970292
Maximum55.360419
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.1 KiB
2025-11-10T18:10:22.807013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.2970292
5-th percentile7.9890455
Q114.308899
median18.788652
Q323.823291
95-th percentile38.029017
Maximum55.360419
Range55.657448
Interquartile range (IQR)9.5143918

Descriptive statistics

Standard deviation8.7560918
Coefficient of variation (CV)0.43988723
Kurtosis1.134251
Mean19.90531
Median Absolute Deviation (MAD)4.8261186
Skewness0.83835885
Sum20044.647
Variance76.669143
MonotonicityNot monotonic
2025-11-10T18:10:22.959828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.132622852
 
0.2%
102
 
0.2%
7.9359464851
 
0.1%
9.3465305281
 
0.1%
7.8979725931
 
0.1%
11.559107321
 
0.1%
25.657894741
 
0.1%
24.712643681
 
0.1%
23.560209421
 
0.1%
22.488038281
 
0.1%
Other values (995)995
97.8%
(Missing)10
 
1.0%
ValueCountFrequency (%)
-0.2970291971
0.1%
0.1003904821
0.1%
0.3258658191
0.1%
0.4169924421
0.1%
0.4792125291
0.1%
0.6420133541
0.1%
0.7831620171
0.1%
1.1896913511
0.1%
1.1970955681
0.1%
1.3752349021
0.1%
ValueCountFrequency (%)
55.360418611
0.1%
50.740794861
0.1%
49.646611351
0.1%
49.099519591
0.1%
48.552427841
0.1%
48.515162121
0.1%
48.005336081
0.1%
47.784814171
0.1%
47.458244331
0.1%
46.911152571
0.1%

raw_wage_gap_ratio_mean
Real number (ℝ)

High correlation  Zeros 

Distinct953
Distinct (%)94.6%
Missing10
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean18.705838
Minimum-0.2970292
Maximum55.360419
Zeros53
Zeros (%)5.2%
Negative1
Negative (%)0.1%
Memory size8.1 KiB
2025-11-10T18:10:23.107598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.2970292
5-th percentile0
Q113.026413
median18.351891
Q323.557553
95-th percentile37.243932
Maximum55.360419
Range55.657448
Interquartile range (IQR)10.53114

Descriptive statistics

Standard deviation9.4698252
Coefficient of variation (CV)0.50624972
Kurtosis0.52989041
Mean18.705838
Median Absolute Deviation (MAD)5.30383
Skewness0.36611295
Sum18836.779
Variance89.677589
MonotonicityNot monotonic
2025-11-10T18:10:23.273918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
053
 
5.2%
34.132622852
 
0.2%
102
 
0.2%
21.764705881
 
0.1%
22.131147541
 
0.1%
23.09644671
 
0.1%
20.714285711
 
0.1%
20.49549551
 
0.1%
20.798319331
 
0.1%
21.194605011
 
0.1%
Other values (943)943
92.7%
(Missing)10
 
1.0%
ValueCountFrequency (%)
-0.2970291971
 
0.1%
053
5.2%
0.1003904821
 
0.1%
0.3258658191
 
0.1%
0.4169924421
 
0.1%
0.4792125291
 
0.1%
0.6420133541
 
0.1%
0.7831620171
 
0.1%
1.1896913511
 
0.1%
1.1970955681
 
0.1%
ValueCountFrequency (%)
55.360418611
0.1%
48.515162121
0.1%
47.784814171
0.1%
46.213268191
0.1%
46.080652451
0.1%
45.72045641
0.1%
45.67591661
0.1%
45.406772921
0.1%
44.448428891
0.1%
43.753796461
0.1%

wage_gap_ratio_median
Real number (ℝ)

High correlation  Missing 

Distinct937
Distinct (%)96.5%
Missing46
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean17.073081
Minimum-7.8
Maximum52.776236
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.6%
Memory size8.1 KiB
2025-11-10T18:10:23.408900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.8
5-th percentile4.441781
Q110.187983
median15.725102
Q321.236522
95-th percentile38.136234
Maximum52.776236
Range60.576236
Interquartile range (IQR)11.048539

Descriptive statistics

Standard deviation9.6928248
Coefficient of variation (CV)0.56772557
Kurtosis0.70426729
Mean17.073081
Median Absolute Deviation (MAD)5.5196825
Skewness0.90140775
Sum16577.962
Variance93.950853
MonotonicityNot monotonic
2025-11-10T18:10:23.791348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.666666677
 
0.7%
6.254
 
0.4%
9.0909090914
 
0.4%
14.285714294
 
0.4%
103
 
0.3%
11.111111113
 
0.3%
6.6666666673
 
0.3%
12.53
 
0.3%
203
 
0.3%
153
 
0.3%
Other values (927)934
91.8%
(Missing)46
 
4.5%
ValueCountFrequency (%)
-7.81
0.1%
-3.1335149861
0.1%
-1.9839957641
0.1%
-1.6778523491
0.1%
-1.3422818791
0.1%
-0.92659117551
0.1%
0.0554938961
0.1%
0.1139601141
0.1%
0.13081341351
0.1%
0.3843870361
0.1%
ValueCountFrequency (%)
52.776235511
0.1%
47.578039181
0.1%
47.260317791
0.1%
46.965487181
0.1%
46.378726411
0.1%
45.677794741
0.1%
45.527421851
0.1%
45.506376321
0.1%
44.422234331
0.1%
44.182205881
0.1%

raw_wage_gap_ratio_median
Real number (ℝ)

High correlation  Missing 

Distinct934
Distinct (%)96.5%
Missing49
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean17.128865
Minimum-7.8
Maximum52.776236
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.4%
Memory size8.1 KiB
2025-11-10T18:10:23.938128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.8
5-th percentile4.5437983
Q110.241954
median15.744134
Q321.240515
95-th percentile38.143983
Maximum52.776236
Range60.576236
Interquartile range (IQR)10.998561

Descriptive statistics

Standard deviation9.6556596
Coefficient of variation (CV)0.5637069
Kurtosis0.71380273
Mean17.128865
Median Absolute Deviation (MAD)5.4978041
Skewness0.91760138
Sum16580.742
Variance93.231762
MonotonicityNot monotonic
2025-11-10T18:10:24.088850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.666666677
 
0.7%
6.254
 
0.4%
14.285714294
 
0.4%
9.0909090914
 
0.4%
103
 
0.3%
12.53
 
0.3%
11.111111113
 
0.3%
203
 
0.3%
153
 
0.3%
6.6666666673
 
0.3%
Other values (924)931
91.5%
(Missing)49
 
4.8%
ValueCountFrequency (%)
-7.81
0.1%
-3.1335149861
0.1%
-1.6778523491
0.1%
-1.3422818791
0.1%
0.0554938961
0.1%
0.1139601141
0.1%
0.3843870361
0.1%
0.5657708631
0.1%
0.5882352991
0.1%
0.7443202531
0.1%
ValueCountFrequency (%)
52.776235511
0.1%
47.578039181
0.1%
47.260317791
0.1%
46.965487181
0.1%
46.378726411
0.1%
45.677794741
0.1%
45.527421851
0.1%
45.506376321
0.1%
44.422234331
0.1%
44.182205881
0.1%

wage_gap_ratio_decile_1
Real number (ℝ)

High correlation  Missing 

Distinct907
Distinct (%)95.2%
Missing64
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean13.984407
Minimum-13.475391
Maximum50
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)3.1%
Memory size8.1 KiB
2025-11-10T18:10:24.246040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-13.475391
5-th percentile1.2672164
Q17.3394849
median12.314301
Q318.889224
95-th percentile33.333333
Maximum50
Range63.475391
Interquartile range (IQR)11.549739

Descriptive statistics

Standard deviation9.8932127
Coefficient of variation (CV)0.70744598
Kurtosis1.0741484
Mean13.984407
Median Absolute Deviation (MAD)5.6476339
Skewness0.93281604
Sum13327.14
Variance97.875658
MonotonicityNot monotonic
2025-11-10T18:10:24.372557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.285714299
 
0.9%
16.666666678
 
0.8%
104
 
0.4%
6.9767441864
 
0.4%
6.6666666673
 
0.3%
6.253
 
0.3%
42.857142863
 
0.3%
12.53
 
0.3%
502
 
0.2%
18.752
 
0.2%
Other values (897)912
89.7%
(Missing)64
 
6.3%
ValueCountFrequency (%)
-13.475390841
0.1%
-9.5416666671
0.1%
-7.8925083271
0.1%
-7.6923076921
0.1%
-7.4020178291
0.1%
-5.7929810581
0.1%
-4.738932651
0.1%
-4.629629631
0.1%
-3.8251366121
0.1%
-2.8886363641
0.1%
ValueCountFrequency (%)
502
0.2%
48.453608251
0.1%
47.368421051
0.1%
46.666666671
0.1%
45.131894221
0.1%
44.655511281
0.1%
44.615384622
0.2%
44.444444442
0.2%
44.209366931
0.1%
441
0.1%

raw_wage_gap_ratio_decile_1
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct884
Distinct (%)92.8%
Missing64
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean13.962281
Minimum-9.5416667
Maximum50
Zeros24
Zeros (%)2.4%
Negative25
Negative (%)2.5%
Memory size8.1 KiB
2025-11-10T18:10:24.499737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9.5416667
5-th percentile0
Q17.3394849
median12.314301
Q318.889224
95-th percentile33.333333
Maximum50
Range59.541667
Interquartile range (IQR)11.549739

Descriptive statistics

Standard deviation9.9018666
Coefficient of variation (CV)0.7091869
Kurtosis1.0214801
Mean13.962281
Median Absolute Deviation (MAD)5.6476339
Skewness0.94533879
Sum13306.054
Variance98.046962
MonotonicityNot monotonic
2025-11-10T18:10:24.639592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
2.4%
14.285714299
 
0.9%
16.666666678
 
0.8%
6.9767441864
 
0.4%
104
 
0.4%
6.253
 
0.3%
42.857142863
 
0.3%
12.53
 
0.3%
6.6666666673
 
0.3%
17.52
 
0.2%
Other values (874)890
87.5%
(Missing)64
 
6.3%
ValueCountFrequency (%)
-9.5416666671
0.1%
-7.8925083271
0.1%
-7.6923076921
0.1%
-5.7929810581
0.1%
-4.738932651
0.1%
-4.629629631
0.1%
-3.8251366121
0.1%
-2.8886363641
0.1%
-2.6881720431
0.1%
-2.6276722091
0.1%
ValueCountFrequency (%)
502
0.2%
48.453608251
0.1%
47.368421051
0.1%
46.666666671
0.1%
45.131894221
0.1%
44.655511281
0.1%
44.615384622
0.2%
44.444444442
0.2%
44.209366931
0.1%
441
0.1%

wage_gap_ratio_decile_9
Real number (ℝ)

High correlation  Missing 

Distinct934
Distinct (%)97.4%
Missing58
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean22.099106
Minimum-27.385038
Maximum63.041013
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)1.7%
Memory size8.1 KiB
2025-11-10T18:10:24.806098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-27.385038
5-th percentile5.5656746
Q117.504349
median22.294754
Q327.083891
95-th percentile40.034722
Maximum63.041013
Range90.426051
Interquartile range (IQR)9.5795419

Descriptive statistics

Standard deviation9.9559676
Coefficient of variation (CV)0.4505145
Kurtosis1.4508334
Mean22.099106
Median Absolute Deviation (MAD)4.7961404
Skewness-0.13338925
Sum21193.042
Variance99.12129
MonotonicityNot monotonic
2025-11-10T18:10:24.946805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105
 
0.5%
11.111111113
 
0.3%
16.666666673
 
0.3%
203
 
0.3%
5.7142857143
 
0.3%
6.9767441863
 
0.3%
7.6923076922
 
0.2%
-3.3333333332
 
0.2%
4.7619047622
 
0.2%
23.312883442
 
0.2%
Other values (924)931
91.5%
(Missing)58
 
5.7%
ValueCountFrequency (%)
-27.385037751
0.1%
-11.656441721
0.1%
-11.428571431
0.1%
-11.111111111
0.1%
-9.6654275091
0.1%
-6.8075117371
0.1%
-5.8112582781
0.1%
-4.7452896021
0.1%
-42
0.2%
-3.4615384621
0.1%
ValueCountFrequency (%)
63.041012881
0.1%
53.49886691
0.1%
52.378604021
0.1%
48.679280261
0.1%
47.79349731
0.1%
46.720915531
0.1%
45.875306291
0.1%
45.702952841
0.1%
45.105328381
0.1%
45.020624631
0.1%

raw_wage_gap_ratio_decile_9
Real number (ℝ)

High correlation  Missing 

Distinct927
Distinct (%)96.7%
Missing58
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean22.057311
Minimum-27.385038
Maximum63.041013
Zeros8
Zeros (%)0.8%
Negative16
Negative (%)1.6%
Memory size8.1 KiB
2025-11-10T18:10:25.090575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-27.385038
5-th percentile4.7619048
Q117.504349
median22.294754
Q327.083891
95-th percentile40.034722
Maximum63.041013
Range90.426051
Interquartile range (IQR)9.5795419

Descriptive statistics

Standard deviation10.031824
Coefficient of variation (CV)0.45480722
Kurtosis1.4185884
Mean22.057311
Median Absolute Deviation (MAD)4.7961404
Skewness-0.15951773
Sum21152.961
Variance100.6375
MonotonicityNot monotonic
2025-11-10T18:10:25.221486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
0.8%
105
 
0.5%
203
 
0.3%
16.666666673
 
0.3%
5.7142857143
 
0.3%
11.111111113
 
0.3%
6.9767441863
 
0.3%
4.7619047622
 
0.2%
8.3333333332
 
0.2%
12.52
 
0.2%
Other values (917)925
91.0%
(Missing)58
 
5.7%
ValueCountFrequency (%)
-27.385037751
0.1%
-11.656441721
0.1%
-11.428571431
0.1%
-11.111111111
0.1%
-9.6654275091
0.1%
-6.8075117371
0.1%
-5.8112582781
0.1%
-4.7452896021
0.1%
-42
0.2%
-3.4615384621
0.1%
ValueCountFrequency (%)
63.041012881
0.1%
53.49886691
0.1%
52.378604021
0.1%
48.679280261
0.1%
47.79349731
0.1%
46.720915531
0.1%
45.875306291
0.1%
45.702952841
0.1%
45.105328381
0.1%
45.020624631
0.1%

Interactions

2025-11-10T18:10:20.860411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.254225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.184533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.082801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.981912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.094276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.965940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.908631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.024657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.948682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.324624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.316473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.167581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.103176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.188244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.076095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.041385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.115455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.036383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.413023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.400776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.264330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.197700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.269918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.175460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.142093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.206885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.129498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.641653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.490409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.362064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.457520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.358328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.266026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.240225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.297042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.247580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.726247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.588510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.464509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.569102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.465039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.391256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.353410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.396995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.349028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.827182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.684165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.565369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.692144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.565154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.487541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.459021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.491275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.442920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:13.911399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.803761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.665249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.791776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.674770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.607112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.756178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.582291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.542167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.002426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.902899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.781757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:16.905564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.765844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.698509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.851095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.686193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:21.638076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:14.096187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.001313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:15.890996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.001513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:17.858775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:18.814535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:19.941197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T18:10:20.765247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-10T18:10:25.340278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
countrycountry_coderaw_wage_gap_ratio_decile_1raw_wage_gap_ratio_decile_9raw_wage_gap_ratio_meanraw_wage_gap_ratio_medianwage_gap_ratio_decile_1wage_gap_ratio_decile_9wage_gap_ratio_meanwage_gap_ratio_medianyear
country1.0001.0000.4520.4420.4230.4540.4580.4440.4460.4550.086
country_code1.0001.0000.4520.4420.4230.4540.4580.4440.4460.4550.086
raw_wage_gap_ratio_decile_10.4520.4521.0000.3850.5850.6960.9990.3840.6050.696-0.402
raw_wage_gap_ratio_decile_90.4420.4420.3851.0000.8540.7460.3881.0000.8900.746-0.569
raw_wage_gap_ratio_mean0.4230.4230.5850.8541.0000.8910.5870.8530.8840.891-0.415
raw_wage_gap_ratio_median0.4540.4540.6960.7460.8911.0000.6950.7460.9241.000-0.523
wage_gap_ratio_decile_10.4580.4580.9990.3880.5870.6951.0000.3860.6060.695-0.403
wage_gap_ratio_decile_90.4440.4440.3841.0000.8530.7460.3861.0000.8900.746-0.569
wage_gap_ratio_mean0.4460.4460.6050.8900.8840.9240.6060.8901.0000.924-0.571
wage_gap_ratio_median0.4550.4550.6960.7460.8911.0000.6950.7460.9241.000-0.523
year0.0860.086-0.402-0.569-0.415-0.523-0.403-0.569-0.571-0.5231.000

Missing values

2025-11-10T18:10:21.776318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-10T18:10:21.912814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-10T18:10:22.087549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

country_codecountryyearwage_gap_ratio_meanraw_wage_gap_ratio_meanwage_gap_ratio_medianraw_wage_gap_ratio_medianwage_gap_ratio_decile_1raw_wage_gap_ratio_decile_1wage_gap_ratio_decile_9raw_wage_gap_ratio_decile_9
0ARGArgentina201711.12474311.1247437.8571437.85714314.28571414.28571410.71428610.714286
1ARGArgentina20189.9537419.95374111.11111111.11111122.22222222.22222211.42857111.428571
2ARGArgentina20199.0364329.03643212.00000012.00000019.64285719.6428578.3333338.333333
3ARGArgentina20206.7571046.7571046.2500006.2500006.6666676.66666710.00000010.000000
4ARGArgentina20217.9359467.9359466.2500006.2500005.0000005.00000010.00000010.000000
5ARGArgentina20229.3465319.3465316.6666676.66666714.28571414.28571411.76470611.764706
6ARGArgentina20237.8979737.8979736.2500006.25000014.28571414.28571410.25641010.256410
7ARGArgentina202411.55910711.5591079.0909099.09090910.00000010.0000008.3333338.333333
8AUSAustralia197525.65789525.65789521.58273421.58273427.80692527.80692532.00346932.003469
9AUSAustralia197624.71264424.71264420.75471720.75471725.43186225.43186229.46768129.467681
country_codecountryyearwage_gap_ratio_meanraw_wage_gap_ratio_meanwage_gap_ratio_medianraw_wage_gap_ratio_medianwage_gap_ratio_decile_1raw_wage_gap_ratio_decile_1wage_gap_ratio_decile_9raw_wage_gap_ratio_decile_9
1007USAUnited States201523.77622423.77622418.88268218.8826829.6774199.67741922.44898022.448980
1008USAUnited States201620.48286620.48286618.14207718.1420778.6746998.67469923.51138423.511384
1009USAUnited States201720.89665720.89665718.17215718.1721579.8850579.88505722.16919722.169197
1010USAUnited States201821.02973221.02973218.91058618.91058613.06209813.06209821.60804021.608040
1011USAUnited States201921.11498321.11498318.47070518.47070514.72392614.72392623.59324823.593248
1012USAUnited States202019.40199319.40199317.65249517.65249510.98039210.98039222.76205122.762051
1013USAUnited States202117.73879117.73879116.86417516.8641759.3984969.39849622.42334722.423347
1014USAUnited States202221.61520221.61520216.98440216.98440212.77683112.77683123.21799323.217993
1015USAUnited States202317.90697717.90697716.38935116.38935110.83743810.83743818.77355318.773553
1016USAUnited States202419.44444419.44444417.28786717.2878678.5578458.55784520.27949320.279493